Search Results for author:
Found 6 papers, 1 papers with code
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization.
A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers
The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers.
Exponentially Many Local Minima in Quantum Neural Networks
Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters.
Quantum exploration algorithms for multi-armed bandits
Identifying the best arm of a multi-armed bandit is a central problem in bandit optimization.
On Second-Order Group Influence Functions for Black-Box Predictions
Often we want to identify an influential group of training samples in a particular test prediction for a given machine learning model.
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares
We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations.
